Build a 'Dining Decision' Micro-App in a Weekend: From Idea to Deployment

Hook: Stop decision fatigue — build a dining micro-app in a weekend

Group chats devolve into endless polls. Friends argue about price, vibe, or dietary needs. You want an answer that actually fits the group, fast. Rebecca Yu did exactly that in a week with 'vibe-coding' and LLM help — now you can recreate her dining app as a weekend micro-app that integrates no-code tools and modern creator stacks. This guide gives you the architecture, recommended stacks, data model, prompts, and deployment options so you ship a prototype before Monday.

The idea, reimagined for 2026

Micro-apps — tiny, focused apps built for a narrow audience or purpose — exploded after 2024 as LLMs and no-code tooling lowered the barrier to entry. By late 2025 this trend matured: creators build 'personal' apps for a few people, using hosted LLM APIs, vector stores, and serverless backends. Rebecca Yu's Where2Eat is a blueprint: a lightweight web app that recommends restaurants based on shared preferences. In 2026, you can build a similar micro-app in a weekend using either a no-code path or a hybrid code approach with managed infra.

What this guide gives you

• Architecture choices for no-code and code-first creators
• Concrete stack recommendations — frontend, backend, LLMs, vector stores
• A pragmatic data model you can copy/paste
• LLM prompt templates and embedding workflow
• Weekend sprint plan and deployment options

High-level architecture options (pick one)

Choose based on speed vs control:

1) No-code-first: fastest, minimal engineering

• Frontend: Glide, Bubble, or Softr — build UI, onboarding, and voting flows visually.
• Data: Airtable or Google Sheets as the canonical store; Supabase if you want relational queries.
• LLM: Use hosted APIs via Zapier/Make or direct API calls from platform webhooks (OpenAI, Anthropic, or hosted open LLM endpoints on Hugging Face). See real-world provider choices and cloud platforms in our cloud platform review.
• Vector search: Use a managed vector add-on (Pinecone, Supabase vector, or Zapier+Pinecone integrations).
• Deploy: Publish from the no-code platform or use a custom domain with DNS.

2) Hybrid code: balance speed and extensibility

• Frontend: React + Vite or Next.js app deployed on Vercel/Cloudflare Pages.
• Backend: Serverless APIs (Vercel/Netlify functions) or a tiny Node/Fastify app on Railway.
• DB: Supabase for auth + Postgres or Firebase for Realtime features.
• LLM: OpenAI/Anthropic for chat completions, plus a vector store (Pinecone/Weaviate/Supabase vector). For patterns around multi-cloud datastores and failover, see guides on architecting read/write datastores.
• Deploy: Vercel for frontend + serverless functions, Supabase for DB & realtime. For edge and latency considerations when calling remote models, consult the latency playbook.

3) Code-first with on-prem or edge model hosting

• When privacy or cost matters: self-host an open model via Hugging Face Endpoints, Replicate, or running Llama-family weights on a small GPU instance. If you need privacy-first personalization or on-device model patterns, check privacy-first personalization playbooks.
• Use an edge runtime (Cloudflare Workers, Fly.io) for ultra-low latency LLM calls via private proxies.

Core features to ship in a weekend (MVP)

1. Quick onboarding: capture name, dietary tags, and 5 preference tags (eg, 'cheap', 'sushi', 'cozy', 'group-friendly').
2. Restaurant feed: list from a seeded dataset or Google Places API.
3. Group aggregation: create a group session and invite friends via short link.
4. Preference fusion: compute a ranked list using weighted preferences + LLM for tie-breakers.
5. Simple voting: thumbs up/down or choose top 3, with final selection highlighted.

Data model — concise and practical

Below is a minimal relational model. Use this with Postgres/Supabase or map to Airtable columns.

-- users
id (uuid)
name (text)
email (text)
prefs (jsonb) -- { 'tags': ['cheap','vegan'], 'radius_km': 5 }

-- groups (a dining session)
id (uuid)
name (text)
host_user_id (uuid)
created_at (timestamp)

-- group_members
group_id (uuid)
user_id (uuid)
joined_at (timestamp)

-- restaurants
id (uuid or external_id)
name (text)
lat (float)
lng (float)
price_tier (int)
categories (text[])
attributes (jsonb) -- { 'vegan_friendly': true }
vector (vector) -- optional embedding for semantic match

-- votes
id (uuid)
group_id (uuid)
user_id (uuid)
restaurant_id (uuid)
vote_type (text) -- 'like'|'dislike'|'ranking'
value (int) -- ranking score or 1/0

-- sessions (cached recommendations)
session_id (uuid)
group_id (uuid)
recommendations (jsonb) -- cached top N
created_at (timestamp)
  

Preference fusion: simple scoring + LLM boost

Start with a deterministic score and use the LLM for context-sensitive final ranking.

Deterministic score

• Distance score: normalized inverse distance to group centroid
• Price match: absolute difference from group's mean price tier
• Category match: Jaccard similarity between restaurant categories and group tag set
• Vote multiplier: boost restaurants with more likes

Combine into a weighted sum. Keep weights configurable.

LLM-assisted tie-breaker

When scores are close, send a concise prompt to the LLM that includes user personas and the top candidates. Let the LLM output a ranked list with short rationales. This is what Rebecca Yu did with 'vibe-coding' — LLMs provide the final human-friendly judgment.

LLM integration: prompts, embeddings, and vector search

Embedding workflow (for semantic matching)

• Embed restaurant descriptions and tags into a vector store.
• Embed user preference blobs (tags + short bio + past likes).
• Compute similarity between group preference aggregate and restaurant vectors to surface semantically relevant options (eg, 'late-night ramen' even if not explicitly tagged).

Prompt templates

Keep prompts small and structured. Example system + user content:

System: You are a concise dining recommender. Return a JSON array of the top 3 restaurants with a 1-line rationale each.

User: Group: Alice (vegan, cozy), Ben (cheap, spicy), Chris (sushi, outdoors). Candidates:
1) 'Sora Ramen' - categories: ['ramen','late-night'] - notes: 'spicy broths, limited vegan options'
2) 'Green Table' - categories: ['vegan','cozy'] - notes: 'good for groups'
3) 'Harbor Sushi' - categories: ['sushi','outdoor'] - notes: 'mid-price, outdoor seating'

Task: Rank and justify best match for the whole group, prefer options that satisfy more members. Output only JSON.
  

In real calls, replace plain text with the group-derived candidate list and include short user preference vectors or tags. For practical examples of turning prompts into runnable micro-app scaffolds, see automating boilerplate generation from prompts.

Provider choices in 2026

• Hosted LLMs: OpenAI (GPT family), Anthropic (Claude family) — easiest integration via REST.
• Managed open models: Hugging Face Endpoints, Replicate — lower cost if you select efficient models.
• Self-host: Running Llama-family weights locally on GPU when privacy is required. For design patterns and tool support as micro-apps scale, read how micro apps are changing developer tooling.

Weekend sprint: 2-day plan (no-code and hybrid tracks)

No-code track

1. Morning, Day 1: Define scope and seed dataset. Create an Airtable base with restaurants and a user table. Sketch screens in Figma or use Glide templates.
2. Afternoon, Day 1: Build UI in Glide/Bubble. Implement group creation and invite link flow. Wire Airtable as datasource.
3. Morning, Day 2: Use Zapier/Make webhook to call LLM for ranking. Connect a Pinecone or Supabase vector integration if available.
4. Afternoon, Day 2: Test with friends, adjust prompt, publish via the platform. Share on TestFlight (for mobile) or custom domain.

Hybrid code track

1. Morning, Day 1: Scaffold Next.js app, wire Supabase auth and DB. Seed restaurants via a CSV import — if you want a reproducible scaffold step, consider tools that generate boilerplate from prompts and CSV seeds (see example).
2. Afternoon, Day 1: Build UI for onboarding and group sessions. Implement deterministic scoring in a serverless function.
3. Morning, Day 2: Add LLM tie-breaker call and embed restaurant descriptions into a vector store (Pinecone/Supabase vector). Hook up embeddings on create/update.
4. Afternoon, Day 2: Deploy frontend to Vercel, backend functions to Vercel or Railway, test, and iterate. For observability patterns to instrument preprod microservices and serverless functions, review modern observability guidance.

Deployment options and cost considerations

Pick based on expected user count and privacy needs.

For a few users (personal micro-app)

• No-code platforms: free tiers or low-cost personal plans.
• Serverless + managed DB: Vercel free + Supabase free tier is sufficient.
• LLM: keep calls small; use smaller models or batching to limit cost. Hosted OpenAI calls will be the main cost factor.

For a small group or small beta

• Use paid Supabase or a small Postgres instance, Pinecone starter plan for vectors, and a mid-tier LLM plan.
• Estimate: $20-150/mo depending on LLM usage, vector queries, and storage.

For privacy or production

• Self-host vector DB (Milvus) and local LLM inference on a GPU to avoid third-party LLM APIs. For privacy-focused personalization, consult the privacy-first personalization playbook.
• Use private domains, enforce access rules, and encrypt sensitive data.

Security, privacy, and ethics

Micro-apps often handle small groups and personal preferences. Respect privacy:

• Only store essential user data. Use short-lived session tokens for group invites.
• Mask PII in LLM prompts where possible. Prefer identifiers and tags instead of full bios.
• Disclose to users when an LLM is used and provide a way to opt out of AI suggestions. For designing permissioned agent and zero-trust flows around generative features, see zero-trust design patterns.

Testing and iteration

Ship early and iterate based on real usage. Metrics to collect:

• Conversion: group created -> final selection
• Click-throughs on recommended restaurants
• User satisfaction: quick thumbs up after a meal
• LLM correctness: track when the LLM suggestion was accepted vs rejected

Common pitfalls and how to avoid them

• Overfitting prompts: Don't hardcode too many assumptions. Keep prompts structured and test with varied groups.
• Too many dependencies: For an MVP, ship with a single vector store and single LLM provider; add complexity only when users justify it.
• Cold start: Seed restaurants from a local CSV or the Places API to avoid empty results — and keep an internal data catalog for your seed dataset (data catalog approaches).

Advanced features to add after the weekend

• Realtime voting and consensus visualizations with Supabase Realtime or Firebase.
• Personalized memory: store past choices to train a lightweight personalization model.
• Multimodal support: allow uploading photos, let the LLM parse images for vibe or menu items.
• Calendar integration and one-click reservations via OpenTable APIs.

Case study snapshot: How Rebecca Yu's approach informs this build

'Vibe-coding' showed that with LLMs and some glue tooling you can ship a useful micro-app quickly. Yu used LLMs to interpret group preferences and make recommendations that felt human. Recreate the same spirit by prioritizing fast feedback loops, small datasets, and conversational outputs for the final decision.

Actionable takeaways — what to do right now

1. Pick your track: no-code for speed, hybrid for control.
2. Create a minimal dataset of 100 restaurants for your city (CSV or Airtable).
3. Build onboarding to collect 5 tagged preferences per user.
4. Implement deterministic scoring first, then add an LLM tie-breaker prompt.
5. Deploy to Vercel/Glide and test with a closed group within 48 hours.

Final notes and 2026 trends to watch

In 2026, expect micro-app toolchains to get even faster: cheaper inference, more efficient vector engines, and richer no-code LLM integrations. The power shift is toward creators who can iterate on user feedback quickly. Rebecca Yu's week-long build is now a baseline — your weekend prototype can be better optimized, more private, and easier to maintain thanks to modern infra. For broader context on launching small, rapid experiments and micro launches, read the micro-launch playbook.

Call to action

Ready to recreate Where2Eat with your spin? Pick a track, follow the weekend sprint above, and publish a working prototype by Sunday night. Share your demo in thecoding.club community or grab our starter template and ready-to-use prompts on GitHub to jumpstart your build. Build small, ship fast, and reclaim your dinner plans.

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